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Related Experiment Video

Updated: Sep 7, 2025

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

Analysis of cyclic recurrent event data with multiple event types.

Chien-Lin Su¹, Feng-Chang Lin²

¹Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.

Japanese Journal of Statistics and Data Science

|June 17, 2022

Summary

This summary is machine-generated.

This study introduces a new statistical model for analyzing recurrent event data with periodic patterns. Incorporating cyclic components significantly improves the predictability of event occurrences compared to traditional models.

Keywords:

Cyclic baseline rate function Fire service Generalized estimating equations Marginal rate model Prediction

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Automated Detection and Analysis of Exocytosis

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Published on: September 11, 2021

Area of Science:

Statistics
Survival Analysis
Time Series Analysis

Background:

Recurrent event data are common in various fields.
Some event processes exhibit cyclic or periodic patterns.
Existing models may not adequately capture these temporal features.

Purpose of the Study:

To propose a semiparametric rate model for multiple recurrent event types with cyclic components.
To estimate regression coefficients and assess model predictability.
To compare the performance of the proposed model against a conventional model.

Main Methods:

Utilized generalized estimating equations for coefficient estimation.
Employed a fully nonparametric estimator to profile the baseline rate function.
Conducted simulation experiments to evaluate finite-sample behavior.
Applied the model to recurrent fire alarm data.

Main Results:

The proposed estimators are consistent and asymptotically Gaussian.
The model with cyclic components demonstrates improved predictability.
The semiparametric rate model effectively handles multiple event types with periodic features.

Conclusions:

The developed semiparametric model is a valuable tool for analyzing recurrent events with cyclic patterns.
Incorporating cyclic components enhances the predictive power for such data.
The model provides a robust framework for understanding and forecasting events with temporal periodicity.